As of 2022, 66% of Internet traffic is generated by video services, among which Netflix and YouTube are the dominant players. By monitoring the traffic volume of individual video services, ISPs (Internet Service Providers) can make appropriate capital investments and determine appropriate peering policies with CDN (Content Delivery Network) providers. Although DPI (Deep Packet Inspection) is widely used to identify network services, packet-level analysis does not scale to the traffic passing through backbone ISPs and is inapplicable to encrypted packets. This paper proposes OLIViS (OSINT-based Lightweight method for Identifying Video Services), a flow-level, label-free method designed for backbone ISPs. OLIViS employs a CNN-VAE (Convolutional Neural Network - Variational AutoEncoder) model to initially identify elephant flows which likely include video service flows and then utilizes OSINT (Open Source INTelligence) data for further services identification. Two datasets are used for evaluation: UTMobileNet2021 and WIDE backbone real trace. The evaluation results show that OLIViS identifies the Netflix and YouTube flows with an accuracy of over 96% in terms of traffic volume. Moreover, OLIViS infers the flow statistics (e.g., mean and peak throughput) of Netflix and YouTube, which closely resemble those inferred with DPI on non-encrypted traffic in a campus network.

MOTIVATION: Although a huge amount of mammalian genomic data does become publicly available, there are still hurdles for biologists to overcome before such data can be fully exploited. One of the challenges for gaining biological insight from genomic data has been the inability to cross-reference transcriptomic and proteomic data using a single informational platform. To address this, we constructed an open-access database that enabled us to cross-reference transcriptomic and proteomic data obtained from immune cells. RESULTS: The database, named RefDIC (Reference genomics Database of Immune Cells), currently contains: (i) quantitative mRNA profiles for human and mouse immune cells/tissues obtained using Affymetrix GeneChip technology; (ii) quantitative protein profiles for mouse immune cells obtained using two-dimensional gel electrophoresis (2-DE) followed by image analysis and mass spectrometry and (iii) various visualization tools to cross-reference the mRNA and protein profiles of immune cells. RefDIC is the first open-access database for immunogenomics and serves as an important information-sharing platform, enabling a focused genomic approach in immunology. AVAILABILITY: All raw data and information can be accessed from http://refdic.rcai.riken.jp/. The microarray data is also available at http://cibex.nig.ac.jp/ under CIBEX accession no. CBX19, and http://www.ebi.ac.uk/pride/ under PRIDE accession numbers 2354-2378 and 2414.